昨天看漫坛音量异常，就冒头说准备今天发个有实货的分散一下课题，被同别的贴一起阵亡了。不过说过了就要做嘛。
偶尔看到西岸某二类厂的 job ad，不禁想了解一下最新的应用 IT skill 的要求，这里随手找出的一打多除个别多不是毕业入门级，多数至少要有点工作经历到 10+ 年。 紫檀里车轱辘谈专业，行业，经验，入门， 工科理科文科。。好像都沾点。软 skill 略过不提。
自己读的头晕目眩，不过坛里个个学富五车懂 IT 干 IT 的应该一眼就了解深度广度以及 job level，嗯，即使自己甩手不干也还有公主在玩的溜溜转。。。下面几个弹着点是读时注意到的。今早有问为何不读博，的确看行业啊。
诸位高人读后，脑袋里转出 5 - 10 个关键词？
首先加州飞飞轻轻松
弹着点
- This position requires either equivalent practical experience or a Bachelor’s degree in a related field.
- machine learning - Relevant PhD can count for up to 2 YOE
- work experience as a data analyst/data scientist
- work experience in analytically-driven roles (e.g. consulting, finance) and / or analytics roles
- experience in related technical areas such as model development, model validation, data science
- Bachelor’s degree in a quantitatively rigorous field like engineering, statistics, math, finance, economics desired
- work experience as a data analyst/data scientist in fraud/identity or credit risk
- relevant experience in technical areas (e.g. data science, data analytics, quantitative finance).
- interest in the exciting field of cybersecurity— NIST Cyber Security Framework, ISO 2700x, SOC1 & 2 (SSAE18), PCI DSS, NIST-800-53, FFIEC Cybersecurity Assessment Tool, SANS Top 20
- industry experience, including 4+ years as DevOps
。。。。。。
闲话少说，the devil is in the details
1
- Proficiency in Kotlin, Dagger, RxJava, Compose, Espresso, Git.
- Android build tooling like Gradle, Proguard or similar.
- Experience with Javascript, Typescript and other web frameworks.
- Prioritizes writing maintainable code and creating a strong testing culture.
- Experience with CI/CD tooling like Buildkite, Fastlane, Docker or similar.
- Nice to have - experience with Python or related frameworks
2
- This position requires either equivalent practical experience or a Bachelor’s degree in a related field
- 4+ years of experience designing, building, launching, and maintaining complex systems
- Familiarity with Python ecosystem - Flask, Mypy, Pytest, etc.
3
- 6+ years of combined work experience in analytically-driven roles (e.g. consulting, finance) and / or analytics roles at other mid-to-large scale Tech organizations
- Strong working knowledge of analytical and data visualization tools including SQL, Looker, and Python for data analysis and modeling
- Experience with modeling unstructured data by leveraging common industry tools (e.g. dbt, Fivetran) to unify multiple heterogeneous sources into one unified single source of truth
- Extensive experience building and driving adoption of KPI frameworks to evaluate business health
4
- 10+ years of software development experience, including at least one of the following: Python,
Kotlin, Rust, Java, C++, GoLang - Expertise in synthesizing complex technical requirements, designs, trade-offs, and capabilities
into clear decisions, and influence product direction - Ability to communicate decisions and practices to the engineering organization effectively
- At least 5+ years of experience in at least two different SRE organizational structures
- At least 5+ years of experience of hands-on work in infrastructure and scaling distributed systems
- At least 5+ years of technical leadership on SWE and Reliability teams focussed on infrastructure,
reliability, and software engineering at scale - Strong hands-on experience with k8s and AWS in a production environment
5
- 6+ years of experience as a machine learning engineer. Relevant PhD can count for up to 2 YOE
- Experience developing machine learning models at scale from inception to business impact
- Proficiency in machine learning with experience in areas such as Generalized Linear Models, Gradient Boosting, Deep Learning, and Probabilistic Calibration. Domain knowledge in credit risk is a plus
- Strong engineering skills in Python and data manipulation skills like SQL
- Experience using large scale distributed systems like Spark or Ray
- Experience using open source projects and software such as scikit-learn, pandas, NumPy, XGBoost, PyTorch, Kubeflow
- Experience with Kubernetes, Docker, and Airflow is a plus
6
- This position requires either equivalent practical experience or a Bachelor’s degree in a related field.
- 3+ years managing high performing engineering teams
- 7+ years in software development roles with proficiency in backend programming languages like Python and front end frameworks like React or AngularJs
- Prior experience in architecting, building, launching and maintaining enterprise scale products.
- Experience with cloud service providers such as AWS
- Experience with data frameworks such as Spark, Kafka, Kubernetes and Airflow
7
- A seasoned Enterprise Security engineer with a strong ability to design, build, evaluate and maintain systems.
- Experience leading investigations and incidents including containment actions and remediation when needed in a cloud heavy environment (AWS preferred).
- Demonstrated experience in common Enterprise Security tooling including but not limited to: SIEM (Elastic/Splunk), EDR (CrowdStrike/SentinelOne), ZTNA (Netskope/Zscaler), SSPM (AppOmni or similar) and IDP (Okta/Onelogin).
- Demonstrated experience and deep subject matter expertise in Corporate systems including but not limited to: Snowflake, Salesforce, Github, Google Workspace, AWS Slack, Notion, Jira, Zendesk, Microsoft 365, and Workday
- Experience with designing and deploying endpoint management and visibility solutions such as Jamf, Intune, and OSQuery.
- Experience leading an enterprise vulnerability management program using tools such as Rapid7, Crowdstrike Spotlight, Qualys or similar.
- Experience with developing native data ingestion and data normalization integrations.
- Familiarity with container orchestration technologies (Kubernetes).
- Experience developing and deploying cloud services using Infrastructure as code with Terraform or similar.
- Experience with Python or similar language to build security tooling.
- Experience building systems with AWS or similar cloud environments.
8
- 2-4 years of work experience as a data analyst/data scientist (credit risk management strongly preferred but not required)
- Extensive experience with SQL and Python, or other scripting languages. Experience with Spark is a plus
- Experience/knowledge with Machine Learning is a plus
9
- EXPERIENCE - 2-5 years work experience as a data analyst/data scientist in fraud/identity or credit risk at a finance/tech company
- TECHNICAL SKILLS - Must be highly proficient in SQL; Experience with Python or R and data visualization tools is a plus; Experience/knowledge with Machine Learning is a plus
- EDUCATION - Bachelor’s or Master’s degree degree in a quantitatively rigorous discipline like analytics, engineering, statistics, math, or economics
10
- 3-6 years of professional experience in related technical areas such as model development, model validation, data science
- This position requires either equivalent practical experience or a Bachelor’s degree in a related field
- Deep and broad knowledge of quantitative modeling in relation to credit underwriting and credit risk management
- Experience with developing models using script languages(e.g., Python) and working with large-scale databases (e.g., SQL)
- Experience with machine learning platforms and frameworks (e.g., scikit-learn, pyspark) and cloud-based coding environments and databases
11
- 0-3 years of relevant experience in technical areas (e.g. data science, data analytics, quantitative finance).
- This position requires either equivalent practical experience or a Bachelor’s degree in a related field
- Strong data-manipulation and model development and implementation skills using Python
12
- 1-3 years of experience in an analytically-driven role (analytics, consulting, finance, etc.)
- Strong working knowledge of SQL and/or experience with Python or R
- Practical and theoretical knowledge of A/B testing
13
- EXPERIENCE - Minimum 1 year of experience in fraud prevention, risk management, or related fields, including prior experience with data analytics in the finance preferred.
- TECHNICAL SKILLS - Proficiency with data analysis using SQL is required. Strong analytical skills with the ability to identify patterns, trends, and anomalies in data are essential. Hands-on experience using a scripting language, such as Python or R, for data analysis is preferred.
- EDUCATION - Bachelor’s degree in a quantitatively rigorous field like engineering, statistics, math, finance, economics desired
14
- Not required, but 1-2 years of experience in risk management, information security, or similar preferred.
- A keen interest in the exciting field of cybersecurity—maybe you’re already familiar with the NIST Cyber Security Framework, ISO 2700x, SOC1 & 2 (SSAE18), PCI DSS, NIST-800-53, FFIEC Cybersecurity Assessment Tool, SANS Top 20, etc.
15
- At least 6 years of industry experience, including 4+ years as DevOps
- Proven coding skills, preferably Python, Golang or Kotlin
- Strong knowledge of Kubernetes
- Experience in public cloud (preferably AWS)
- Design and development of highly available systems (99.99%+ SLA)
- Infrastructure as a code - preferably Terraform
- Extensive knowledge of monitoring solutions (e.x. Prometheus, ELK)
Additional skills:
- Developing multi-region cloud solutions
- Solid experience in cloud networking - both on cloud and within Kubernetes
- Experience with databases and optimizing database usage within infrastructure
- Good knowledge of SDLC tooling, including CICD systems, artifact and container repositories
